Data Science in Hong Kong

Even if you're not a data scientist or statistician or business analyst, you need to become familiar with the concept of data science (or statistics, or… etc). Here's why:

from http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

Most obviously, data science requires statistical thinking and knowledge. This means, fundamentally, turning data into inferences about phenomena.

How can you develop statistical thinking? I got a Master's degree and then a PhD in Statistics, but maybe not everyone has the time to do that…

You need knowledge of some statistical/machine learning techniques, but actually you don't need a deep theoretical understanding of how they work. What matters is to know the strengths and weaknesses of each technique, and how to validate the choice of a statistical model.

Data is an electronic commodity now, and especially in large quantities (did someone say big data??), demands extensive programming to handle it.

There's no need to learn C or Java… It's enough to learn a friendly general-purpose language like python or the domain-specific language R. I have used R almost every day for over a decade.

And now the biggest commercial provider of R has been bought by Microsoft!

http://blogs.technet.com/b/machinelearning/archive/2015/04/06/microsoft-closes-acquisition-of-revolution-analytics.aspx

from http://sloanreview.mit.edu/article/getting-value-from-your-data-scientists/

The University of Hong Kong has a well-deserved reputation as being one of Asia's top research universities. I spent three years at the School of Public Health as a Post-Doctoral Fellow focusing on the statistics of influenza prevalence.

Around two years ago, a group of like-minded people started an informal organisation dedicated to Open Data, i.e. data that is “that can be freely used, reused and redistributed by anyone”. We believe that the government, in particular, should release whatever data it holds in an open way.

Still going strong, the ODHK Facebook group has over 1500 members, and only yesterday hosted a talk given by Hong Kong's Office of the Government Chief Information Officer.

We are seeing some progress in the government's attitude to opening its data, although it continues to fall behind in particular with regards to company registry and lands data. Hong Kong's economy and development will massively benefit from a strong foundation of governmental open data.

http://www.scmp.com/lifestyle/technology/innovation/article/1740277/more-apps-way-opening-government-data

Hong Kong's startup scene continues to burgeon.

DemystData was one of the first of the current era of tech startups, having been founded in 2010. Its mission was to provide credit scores for people who traditionally don't have one, in order to unlock finance for poorer people all over the world. We did this by unlocking new data sources (including social network data from Facebook, Twitter et al) and sophisticated statistical models.

So far DemystData have raised over US$5 million.

I am currently at EY Hong Kong, joining our relatively new Enterprise Intelligence team.

We define Enterprise Intelligence as an organization’s ability to optimize its performance by:

Identifying relevant information

Analyzing it in such a way that produces insights that are deeper than their competitors’

Being able to act upon these insights

http://performance.ey.com/2013/08/13/enterprise-intelligence-aligning-needs-and-goals/